Measuring the energy intensity of domestic activities from smart meter data

Stankovic, L. and Stankovic, V. and Liao, J. and Wilson, C. (2016) Measuring the energy intensity of domestic activities from smart meter data. Applied Energy, 183. pp. 1565-1580. ISSN 0306-2619 (https://doi.org/10.1016/j.apenergy.2016.09.087)

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Abstract

Household electricity consumption can be broken down to appliance end-use through a variety of methods such as modelling, submetering, load disaggregation or non-intrusive appliance load monitoring (NILM). We advance and complement this important field of energy research through an innovative methodology that characterises the energy consumption of domestic life by making the linkages between appliance end-use and activities through an ontology built from qualitative data about the household and NILM data. We use activities as a descriptive term for the common ways households spend their time at home. These activities, such as cooking or laundering, are meaningful to households' own lived experience. Thus, besides strictly technical algorithmic approaches for processing quantitative smart meter data, we also draw on social science time use approaches, interview and ethnography data. Our method disaggregates a households total electricity load down to appliance level and provides the start time, duration, and total electricity consumption for each occurrence of appliance usage. We then make inferences about activities occurring in the home by combining these disaggregated data with an ontology that formally species the relationships between electricity-using appliances and activities. We also propose two novel standardised metrics to enable easy quantifiable comparison within and across households of the energy intensity and routine of activities of interest. Finally, we demonstrate our results over a sample of ten households with an in-depth analysis of which activities can be inferred with the qualitative and quantitative data available for each household at any time, and the level of accuracy with which each activity can be inferred, unique to each household. This work has important applications from providing meaningful energy feedback to households to comparing the energy efficiency of households' daily activities, and exploring the potential to shift the timing of activities for demand management.

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